The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour.

The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

Many SAR satellites such as the ALOS-2 satellite and Sentinel-1A satellite can be used in Interferometric Synthetic Aperture Radar (InSAR) to identify landslides. As their wavelengths are different, they can perform differently in the same area. In this study, we selected the alpine canyon heavy forest area of the Baishugong–Shangjiangxiang section of the Jinsha River with a strong uplift of faults and folds as the study area. The Small Baseline Subset (SBAS)–InSAR was used for landslide identification to compare the reliability and applicability of L-band ALOS-2 data and C-band Sentinel-1A data. In total, 13 potential landslides were identified, of which 12 potential landslides were identified by ALOS-2 data, two landslides were identified by Sentinel-1A data, and the Kongzhigong (KZG) landslide was identified by both datasets. Then, the field investigation was used to verify the identification results and analyze the genetic mechanism of four typical landslides. Both the Duila (DL) and KZG landslides are bedding slip, while the Jirenhe (JRH) and Maopo (MP) landslides are creep–pull failure. Then, the difference between ALOS-2 and Sentinel-1A data on KZG landslide was compared. A total of 35,961 deformation points on the KZG landslide were obtained using ALOS-2 data, which are relatively dense. Meanwhile, a total of 7715 deformation points were obtained by Sentinel-1A data, which are relatively scattered and seriously lacking, especially in areas with dense vegetation coverage. Comparing the advantages of ALOS-2 and Sentinel-1A data and the identification results of potential landslides, the reliability and applicability of ALOS-2 data in the identification of potential landslides in areas with dense vegetation cover and complex geological conditions were confirmed from the aspects of vegetation cover, topography, field investigation, and comparative analysis of typical landslides.

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Comparative Study on Potential Landslide Identification with ALOS-2 and Sentinel-1A Data in Heavy Forest Reach, Upstream of the Jinsha River

The identification and early warning of potential landslides can effectively reduce the number of casualties and the amount of property loss. At present, interferometric synthetic aperture radar (InSAR) is considered one of the mainstream methods for the large-scale identification and detection of potential landslides, and it can obtain long-term time-series surface deformation data. However, the method of identifying anomalous deformation areas using InSAR data is still mainly manual delineation, which is time-consuming, labor-consuming, and has no generally accepted criterion. In this study, a two-stage detection deep learning network (InSARNet) is proposed and used to detect anomalous deformation areas in Maoxian County, Sichuan Province. Compared with the most commonly used detection models, it is demonstrated that the InSARNet has a better performance in the detection of anomalous deformation in mountainous areas, and all of the quantitative evaluation indexes are higher for InSARNet than for the other models. After the anomalous deformation areas are identified using the proposed model, the possible relationship between the anomalous deformation areas and potential landslides is investigated. Finally, the fact that the automatic and rapid identification of potential landslides is the inevitable trend of future development is discussed.

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A New Deep Learning Neural Network Model for the Identification of InSAR Anomalous Deformation Areas

Deep learning in food category recognition

Humans have paid a lot of attention to environmental geological challenges in recent years. Landslides, being one of the most prevalent geological disasters, are characterized by their suddenness and destructiveness. Southwest China is prone to landslides and debris flows due to its unique geological structure. This paper uses landslides in southwest China as an example, focusing on research on landslide initiation mechanisms and outlining modern landslide monitoring devices and prediction models. Landslides are caused by external variables such as persistent precipitation, groundwater movement, and significant seismic activity, as well as interior reasons such as fine particle rearrangement and the action of positive pore water. The reduction of the friction coefficient of the shear surface, which is induced by the increase of the shear rate, the supercritical carbon dioxide and superheated steam of the shear zone, and the mineral recrystallization process on the shear surface, all have an important impact on reducing the friction coefficient of the shear surface, is a key factor in the occurrence of high-speed remote landslides. Real-time landslide monitoring using space-air-ground and acoustic emission technology, as well as the creation of machine learning-based forecast models, have aided in the research of landslide development and early warning.

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Environmental Geological Problems in Southwest China: A Case Study from the Researches of Regional Landslide Hazards

Detection of Active Landslides in Southwest China using Sentinel-1 and ALOS-2 Data

EAGNet: A method for automatic extraction of agricultural greenhouses from high spatial resolution remote sensing images based on hybrid multi-attention

Use of L-band SAR data for Monitoring Glacier Surging next to Aru Lake

Given the safety and stability problems and the lack of flexibility caused by the disorderly access of large-scale electric vehicles (EVs) to the power grid, this paper fully considers the flexibility of the charge-discharge mode of EVs and taps more adjustable capacity. An EV aggregation model that considers the flexibility of the charge-discharge mode. Firstly, clustering is performed based on the characteristic parameters of EVs, and according to the flexibility difference in the cluster, the corresponding charge-discharge mode is allocated to expand the feasible area of EV energy and power. The simulation results demonstrate the effectiveness of the aggregated model in improving the adjustable capacity of EVs.

Hongzhou Li

Papers

Detection of Active Landslides in Southwest China using Sentinel-1 and ALOS-2 Data

EAGNet: A method for automatic extraction of agricultural greenhouses from high spatial resolution remote sensing images based on hybrid multi-attention

Use of L-band SAR data for Monitoring Glacier Surging next to Aru Lake

EV Aggregation Method Considering Flexibility of Multiple Charge-discharge Modes